In-situ metrology, inverse analysis and first-principle modelling for the physics- and data-based prediction of highly non-linear material behaviour and failure in manufacturing technology

切断制造工艺（例如剪切切割）使用机械机制来打破工件的材料凝聚力。在此过程中，高度非线性的变形和复杂的热粘塑性材料效应以及材料损伤和材料失效的机制都会发生，这些都是基于物理状态变量和材料微观结构之间的相互作用，决定了制造过程的质量。缺乏对状态变量（例如温度、应变和应力场）以及工艺变量（例如切削速度、切削间隙和切削刃几何形状）之间的相互依赖性的理解，导致所有现有的建模方法不能以可概括的预测方式使用，并且在局部狭窄的工艺窗口之外失去其有效性。虽然该过程的典型的高度非线性材料行为和由此产生的广泛的状态变量严重限制了现有局部表征和建模方法的有效性，但正是这种过程性质被申请人解释为关于过程和材料行为的综合信息的机会和来源。具体而言，该研究项目将联合收割机结合机器学习的新概率方法，用于逆材料参数和材料模型识别，基于高保真有限元方法的第一原理建模方法以及高分辨率原位测量方法，以一种新的方式实现材料行为的准确和全局预测，即，在整个过程参数空间中有效并且原则上也可转移到其它过程的预测。虽然以前的研究方法并不总是联合收割机现代数值建模和分析方法和创新的实验测量和评估技术相结合，而是认为他们主要是在彼此隔离，只有提出的组合物理和数据为基础的方法允许充分利用潜力方面的预测和概括的材料和工艺建模。

Severing manufacturing processes such as shear cutting use mechanical mechanisms to break up the material cohesion of work pieces. In this process, highly non-linear deformations and complex thermo-visco-plastic material effects as well as mechanisms of material damage and material failure occur, which are based on the interaction between physical state variables and the material microstructure, which decisively determine the manufacturing process quality. The lack of understanding of the interdependencies between state variables such as temperature, strain and stress field as well as process variables such as cutting speed, cutting clearance and cutting edge geometry has the consequence that all existing modelling approaches cannot be used in a generalizable predictive way and lose their validity outside a local, narrow process window. While the highly non-linear material behaviour typical for the process and the resulting broad spectrum of state variables severely limits the validity of existing local characterisation and modelling approaches, it is precisely this process property that is interpreted by the applicants as an opportunity and source of comprehensive information on process and material behaviour. Specifically, the research project will combine novel probabilistic methods of machine learning for inverse material parameter and material model identification, first-principle modelling approaches based on high-fidelity finite element methods as well as high-resolution in-situ measurement methods in a novel way in order to enable accurate and global predictions of material behaviour, i.e. predictions that are valid in the entire process parameter space and in principle also transferable to other processes. While previous research approaches do not consistently combine modern numerical modelling and analysis methods and innovative experimental measurement and evaluation techniques, but rather consider them predominantly in isolation from each other, only the proposed combined physics- and data-based approach allows to fully exploit potentials with regard to predictive and generalisable material and process modelling.